Machine Learning Scientist (AI)

TL;DR

Machine Learning Scientist (AI): Pioneering the next generation of AI systems capable of reasoning like a scientist, designing novel frameworks that push the boundaries of LLM-based reasoning methods and implementing scalable frameworks with an accent on integrating symbolic and statistical approaches to generate, test, deploy and optimize scientific hypotheses. Focus on exploring in-context learning, self-reflection, and adaptive reasoning to build scalable model prototypes for frontier scientific problems.

Location: Cambridge, MA USA

Salary: $176,000-$304,000 USD per year, along with bonus potential and generous early equity.

Company

hirify.global is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.

What you will do

• Design and formalize frameworks for scientific reasoning with LLMs, including structured prompting, reasoning chains, and test-time compute.
• Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows.
• Build scalable model prototypes that can be deployed to solve frontier scientific problems.
• Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches.

Requirements

• PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields.
• Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, and related toolkits).
• Expertise in LLM reasoning methods: in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning.
• Ability to balance theoretical research with practical ML engineering to deliver scalable solutions.

Nice to have

• Research experience in causal reasoning, symbolic AI, or probabilistic programming.
• Contributions to open-source LLM reasoning frameworks.
• Familiarity with scientific discovery pipelines in chemistry, biology, or materials science.
• Experience with multimodal reasoning (e.g., combining text, image, and experimental data).
• Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).

Culture & Benefits

• Committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
• Bonus potential and generous early equity.